ShinyEvents: harmonizing longitudinal data for real-world survival estimation

Abstract Longitudinal data analysis of the patient’s treatment course is critical to uncovering variables that influence outcomes. However, existing tools have significant limitations in integrating multilayered time-series data, particularly in linking treatment events with survival outcomes. Here,...

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Detaylı Bibliyografya
Asıl Yazarlar:	Alyssa Obermayer, Joshua Davis, Divya Priyanka Talada, Mingxiang Teng, Steven Eschrich, Vivien Yin, Daniel Spakowicz, Dipankor Chatterjee, Robert J. Rounbehler, Michelle L. Churchman, Ahmad A. Tarhini, Xuefeng Wang, Sumati Gupta, Joseph Markowitz, Jeremy Goecks, Roger Li, Rodrigo Rodrigues Pessoa, Brandon J. Manley, Aik-Choon Tan, G. Daniel Grass, Dung-tsa Chen, Timothy I. Shaw
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Nature Portfolio 2026-01-01
Seri Bilgileri:	npj Precision Oncology
Online Erişim:	https://doi.org/10.1038/s41698-025-01212-0
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ShinyEvents: harmonizing longitudinal data for real-world survival estimation

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